A Systematic Literature Review on the Applications, Models, Limitations, and Future Directions of Generative Adversarial Networks

Abstract

Generative adversarial networks (GANs), a subset of deep learning, have demonstrated breakthrough performance in domains such as computer vision (CV) and natural language processing (NLP), particularly in surveillance, autonomous driving, and automated programing assistance. Based on game theory principles, GANs utilize a generator–discriminator architecture to produce high-quality synthetic data. This study conducts a systematic literature review (SLR) to comprehensively assess the development, applications, limitations, and security-related advancements of GANs. It examines foundational models and key architectural variants, providing a critical evaluation of their roles in NLP and CV. This research explores the integration of GANs into the domain of security, highlighting their applications in information security, cybersecurity, and artificial intelligence (AI)-driven defense mechanisms. The study also discusses prominent evaluation metrics such as inception score (IS), Fréchet inception distance (FID), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR) to assess GAN performance. Key strengths of GANs, including their ability to generate high-resolution data and support domain adaptation, are emphasized as driving factors for their continued evolution and adoption.